Stroke is the third leading cause of death in the US and western countries after myocardial infarct and cancer, and the leading cause of disability. Besides the dramatic decrease of the individuals' quality of life, stroke has an evident socio-economic impact with costs of 35 to 50 thousand US $ per stroke survivor per year.
Concerning these facts, there is a strong need for an effective treatment of stroke patients. During the last decade, studies of recanalizing drugs and neuroprotectants in acute ischemic stroke patients have shown promising results. However, this treatment has to be applied within a narrow window of time following the stroke. After six hours the relative risk of the therapy outweighs its benefits. Although the treatment is helpful when applied to patients with acute ischemic stroke, it is hazardous when applied to patients with an acute cerebral bleeding, e.g., haemorrhagic stroke, or event with a disposition for cerebral bleeding.
Both time-pressure and the hazardous effect on patients with cerebral bleeding demand a fast, qualified, differential diagnosis of the stroke based on adequate imaging and image reading techniques. However, these techniques are available to very few medical specialists, hence currently only 3-4% of patients with acute ischemic stroke are treated with an adequate therapy like intravenous thrombolysis.
In CT imaging, an acute haemorrhagic stroke can be characterized by typical gray value characteristics that change in the course of the disease. In an acute phase the stroke region is depicted as a hyperdense, i.e., relatively brighter area, whereas a chronic haemorrhagic stroke appears as a hypodense, i.e., relatively darker area. These typical gray values demand for image processing approaches like thresholding, clustering and region growing.
In the article entitled “Image Analysis and 3-D Visualization of Intracerebral Brain Hemorrhage ”, Dhawan et al, proceedings of Sixth Annual IEEE Symposium on Computer-Based Medical Systems (13-16 Jun. 1993). pages 140 -145 , Dhawan et al propose a semi-automatic approach to detect intracerebral haemorrhage based on CT images, A k-means clustering algorithm subdivides the entire image into foreground and background. On the resulting binary image , the user selects an adequate seed point for a subsequent region growing algorithm that delineates the intracerebral haemorrhage.
A more automated rule-based approach is presented by M. Matesin et al. in the article entitled “A rule-based approach to stroke lesion analysis from CT images”, in Image and Signal Processing and Analysis, 2001, pages 219-223. Here image features like brightness and symmetry, relative to the symmetry axis of the brain, of an extracted region are used to classify the image into background, skull, cerebrospinal fluid, gray/white matter and stroke. An area of stroke that is not symmetric with respect to the symmetry axis of the brain is labeled as hypodense by the authors. These assumptions may be true for an ischemic stroke, but do not correctly describe an acute haematoma.